A Robust Statistical method to Estimate the Intervention Effect with Longitudinal Data

  • Mohammad M Islam Utah Valley University, USA
  • Erik L Heiny Utah Valley University, USA
Keywords: Interrupted time series analysis, Intervention effect, autocorrelation, robust median test


Segmented regression is a standard statistical procedure used to estimate the effect of a policy intervention on time series outcomes. This statistical method assumes the normality of the outcome variable, a large sample size, no autocorrelation in the observations, and a linear trend over time. Also, segmented regression is very sensitive to outliers. In a small sample study, if the outcome variable does not follow a Gaussian distribution, then using segmented regression to estimate the intervention effect leads to incorrect inferences. To address the small sample problem and non-normality in the outcome variable, including outliers, we describe and develop a robust statistical method to estimate the policy intervention effect in a series of longitudinal data. A simulation study is conducted to demonstrate the effect of outliers and non-normality in the outcomes by calculating the power of the test statistics with the segmented regression and the proposed robust statistical methods. Moreover, since finding the sampling distribution of the proposed robust statistic is analytically difficult, we use a nonparametric bootstrap technique to study the properties of the sampling distribution and make statistical inferences. Simulation studies show that the proposed method has more power than the standard t-test used in segmented regression analysis under the non-normality error distribution. Finally, we use the developed technique to estimate the intervention effect of the Istanbul Declaration on illegal organ activities. The robust method detected more significant effects compared to the standard method and provided shorter confidence intervals.

Author Biographies

Mohammad M Islam, Utah Valley University, USA
Department of Mathematics, Utah Valley University, Assistant professor of Biostatistics 
Erik L Heiny, Utah Valley University, USA
Department of Mathematics, Utah Valley University, Associate Professor of Statistics 


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How to Cite
Islam, M. M., & Heiny, E. L. (2020). A Robust Statistical method to Estimate the Intervention Effect with Longitudinal Data. Statistics, Optimization & Information Computing, 8(1), 318-327. https://doi.org/10.19139/soic-2310-5070-811
Research Articles